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CN119207059A - A dam crack monitoring and early warning system and method - Google Patents

A dam crack monitoring and early warning system and method Download PDF

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Publication number
CN119207059A
CN119207059A CN202411708878.0A CN202411708878A CN119207059A CN 119207059 A CN119207059 A CN 119207059A CN 202411708878 A CN202411708878 A CN 202411708878A CN 119207059 A CN119207059 A CN 119207059A
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crack
dam
coefficient
data
distance
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黄习习
蔡国军
张艳
黄坤
高润喜
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Anhui Jianzhu University
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Abstract

The invention discloses a dam crack monitoring and early warning system and a dam crack monitoring and early warning method, which relate to the technical field of crack monitoring, wherein dam related data are acquired through a data acquisition module, a dam comprehensive distance coefficient is calculated through processing of a data processing module, a dam analysis module is used for comparing the dam comprehensive distance coefficient with a dam comprehensive distance coefficient in a mode of setting a threshold value to determine whether a dam has a crack, a horizontal proportion coefficient and a vertical proportion coefficient are set for comparing the dam with each other again to determine the dam crack type, a crack quality judgment module is used for acquiring crack related index data, the crack comprehensive index is calculated to obtain a crack comprehensive index size coefficient, a crack standard judgment result is output through a crack standard judgment model, an image is matched through an image acquisition processing module to obtain a final crack image, and an alarm prompt is carried out on the grade type of the image through an early warning module, so that the functions of detecting the dam crack grade type and early warning can be realized.

Description

Dam crack monitoring and early warning system and method
Technical Field
The invention relates to the technical field of crack monitoring, in particular to a dam crack monitoring and early warning system and method.
Background
In recent years, economic losses caused by river dikes due to typhoon and storm are increasing, and safety inspection requirements of flood dikes and seawalls are also becoming more and more strict in various places.
Most of dam crack monitoring modes at present are combined with manual observation and manual inspection to ensure the safety of the dam. The manual observation and the manual inspection are affected by the dam body area environment, weather and light, the observation precision is low, the labor intensity is high, and the observation is greatly affected by the environment, so that the working efficiency of dam crack monitoring is low.
Disclosure of Invention
In order to solve the defects in the background art, the invention aims to provide a dam crack monitoring and early warning system and method which can detect the grade type of the dam crack and perform early warning.
In a first aspect, the invention provides a dam crack monitoring and early warning system, comprising:
The crack analysis module is used for setting a dam comprehensive distance threshold, comparing a dam comprehensive distance coefficient with the dam comprehensive distance threshold, determining whether a crack exists in the dam according to a comparison result, if the crack exists in the dam, determining the type of the crack according to a re-comparison result by setting a horizontal proportionality coefficient and a vertical proportionality coefficient, transmitting different types of crack signals to the image acquisition processing module, and transmitting a crack quality judging signal to the crack quality judging module;
The calculation process of the dam comprehensive distance coefficient is as follows:
Wherein Si is a dyke horizontal distance, ci dyke vertical distance is Zi is a dyke comprehensive distance coefficient, S0 is a preset standard horizontal coefficient, C0 is a preset standard vertical coefficient, k 1 is a horizontal distance influence coefficient, k 2 is a vertical distance influence coefficient, ln () is a logarithmic function taking a natural constant e as a base number, t is a preset proportional coefficient, wherein i is a dyke related data acquisition frequency index, and i=1, 2, 3, & gt, n and n are the total number of dyke related data acquisition frequencies;
The crack quality judging module is used for collecting the related index data of the crack, including the crack size data and the crack inclination angle, preprocessing the related index data of the crack to obtain processed related index data of the crack, carrying out identification processing on the processed related index data of the crack, carrying out crack comprehensive index calculation by using the identified related index data of the crack to obtain a comprehensive index size coefficient of the crack, inputting the comprehensive index size coefficient of the crack and the crack inclination angle into a pre-established crack standard judging model, outputting to obtain a crack standard judging result, and sending the crack standard judging result to the image collecting and processing module;
The image acquisition processing module is used for acquiring images corresponding to different types of crack signals as a first image and images corresponding to a crack standard discrimination result as a second image, matching the first image with the second image to obtain a final crack image, and transmitting the final crack image to the early warning module;
And the early warning module is used for carrying out voice warning prompt according to the grade type of the final crack image.
With reference to the first aspect, in certain implementation manners of the first aspect, the system further includes a process that the data processing module performs coordinate distance processing according to the dam related data:
the data acquisition module is used for acquiring dyke related data and transmitting the dyke related data to the data processing module, wherein the dyke related data comprises a plurality of dyke horizontal direction coordinates and a plurality of dyke vertical direction coordinates;
the data processing module is used for carrying out coordinate distance processing according to the dam related data to obtain a dam horizontal distance and a dam vertical distance, carrying out dam comprehensive distance calculation on the dam horizontal distance and the dam vertical distance to obtain a dam comprehensive distance coefficient, and sending the dam comprehensive distance coefficient to the crack analysis module.
With reference to the first aspect, in certain implementation manners of the first aspect, the system further includes a process that the data processing module of the data processing module performs coordinate distance processing according to the dam related data:
Determining coordinates of points with the largest distance between two horizontal coordinates according to a plurality of dykes and dams horizontal direction coordinates in dykes and dams related data, marking the coordinates as two horizontal extreme points, and determining dykes and dams horizontal distance according to the two horizontal extreme point coordinates;
And determining the coordinates of the point with the largest distance between the two vertical coordinates according to the plurality of dykes and dams vertical direction coordinates in the dykes and dams related data, marking the coordinates as two vertical extreme points, and determining the dykes and dams vertical distance according to the two vertical extreme point coordinates.
With reference to the first aspect, in certain implementation manners of the first aspect, the system further includes an analysis process of the crack analysis module including:
setting a dam comprehensive distance threshold Z0;
if Zi is less than or equal to Z0, judging that the dam does not have cracks, and sending a crack-free signal to an early warning module by a crack analysis module;
If Zi is greater than Z0, judging that the dam has cracks, and further determining the type of the cracks;
Setting a horizontal proportionality coefficient P1 and a vertical proportionality coefficient P2, wherein P2> P1>1;
If it is Judging that the dam crack at the moment is a horizontal crack, sending a horizontal crack signal to an image acquisition processing module,
If it isJudging that the dam crack at the moment is a vertical crack, sending a vertical crack signal to an image acquisition processing module,
If it isJudging that the dam crack at the moment is a cracking crack, and sending a tortoise crack signal to an image acquisition and processing module;
and, sending a crack quality determination signal to the crack quality determination module.
With reference to the first aspect, in certain implementation manners of the first aspect, the system further includes a process of performing an identification process on the processed fracture size data by the fracture quality determination module:
The fracture length data is labeled Lj, the fracture width data is labeled Dj, the fracture depth data is labeled Sj, where j is the number of acquisitions of the fracture size data and j=1, 2, 3,..m, m is the total number of acquisitions of the fracture size data.
With reference to the first aspect, in certain implementation manners of the first aspect, the system further includes a crack comprehensive index calculation process of the crack quality determination module as follows:
wherein Fj is a crack comprehensive index size coefficient, R1 is a crack length influence coefficient, R2 is a crack width influence coefficient, R3 is a crack depth influence coefficient, and alpha is a preset correlation coefficient.
With reference to the first aspect, in certain implementation manners of the first aspect, the system further includes a process that a crack standard discrimination model of the crack quality discrimination module is trained based on an artificial intelligence model:
Acquiring preset standard crack related indexes through a server, wherein the crack related indexes comprise preset standard crack sizes and preset standard crack dip angles;
Training the artificial intelligent model through preset standard crack related indexes, and obtaining and storing to obtain a crack standard discrimination model.
With reference to the first aspect, in certain implementation manners of the first aspect, the system further includes that the fracture criteria discrimination result includes a first-stage fracture, a second-stage fracture and a third-stage fracture;
The types of the final crack image comprise a first-stage horizontal crack, a second-stage horizontal crack, a third-stage horizontal crack, a first-stage vertical crack, a second-stage vertical crack, a third-stage vertical crack, a first-stage tortoise crack, a second-stage tortoise crack and a third-stage tortoise crack.
In order to achieve the above purpose, the invention discloses a dam crack monitoring and early warning method, which comprises the following steps:
Acquiring dyke related data, including a plurality of dyke horizontal direction coordinates and a plurality of dyke vertical direction coordinates, carrying out coordinate distance processing on the dyke related data, and then carrying out dyke comprehensive distance calculation to obtain dyke comprehensive distance coefficients;
The calculation process of the dam comprehensive distance coefficient is as follows:
Wherein Si is a dyke horizontal distance, ci dyke vertical distance is Zi is a dyke comprehensive distance coefficient, S0 is a preset standard horizontal coefficient, C0 is a preset standard vertical coefficient, k 1 is a horizontal distance influence coefficient, k 2 is a vertical distance influence coefficient, ln () is a logarithmic function taking a natural constant e as a base number, t is a preset proportional coefficient, wherein i is a dyke related data acquisition frequency index, and i=1, 2, 3, & gt, n and n are the total number of dyke related data acquisition frequencies;
setting a dyke comprehensive distance threshold, judging that no crack exists if the dyke comprehensive distance coefficient is smaller than or equal to the dyke comprehensive distance threshold, and judging that the crack exists if the dyke comprehensive distance coefficient is larger than the dyke comprehensive distance threshold;
Setting a horizontal proportion coefficient and a vertical proportion coefficient, comparing the set coefficient with the dam comprehensive distance coefficient according to the product of the set coefficient and the dam comprehensive distance threshold value, and determining the crack type according to the comparison result, wherein the vertical proportion coefficient is larger than the horizontal proportion coefficient and larger than one;
Acquiring and preprocessing crack related index data to obtain processed crack related index data, wherein the crack related index data comprises crack size data and a crack inclination angle, and performing crack comprehensive index calculation by using the crack size data to obtain a crack comprehensive index size coefficient;
inputting the size coefficient of the comprehensive crack index and the inclination angle of the crack into a pre-established crack standard judging model, outputting to obtain a crack standard judging result, matching the image of the crack standard judging result with images of different crack types to obtain a final crack image, and carrying out voice alarm prompt according to the grade type of the final crack image.
The invention has the beneficial effects that:
The invention collects dam related data through a data acquisition module, processes and calculates the dam comprehensive distance coefficient through a data processing module, compares the dam comprehensive distance coefficient with the dam comprehensive distance coefficient through a set threshold value mode through a crack analysis module, determines whether a crack exists in the dam, compares the dam crack type again through a set horizontal proportion coefficient and a set vertical proportion coefficient, acquires crack related index data through a crack quality judging module, calculates the crack comprehensive index to obtain a crack comprehensive index size coefficient, outputs a crack standard judging result through a crack standard judging model, carries out image matching through an image acquisition processing module to obtain a final crack image, carries out alarm prompt on the grade type of the image through an early warning module, and realizes the functions of detecting the grade type of the dam crack and carrying out early warning.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to those skilled in the art that other drawings can be obtained according to these drawings without inventive effort;
FIG. 1 is a schematic diagram of a system architecture of the present invention;
FIG. 2 is a schematic flow chart of the method of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one:
the following description is made of the relevant terms related to the embodiments of the present application:
‌ the dam is a waterproof and water-blocking building and structure, and is mainly divided into two main categories of earth-rock dams and concrete dams. ‌ A
The dam is a building for water-proof and water-blocking, and is designed and constructed to prevent erosion of the land by water flow and protect surrounding areas from flood. The types of dikes mainly include earth-rock dikes and concrete dikes. Earth-rock dams are typically constructed using earth or stone, and are typically constructed across large rivers using common and inexpensive materials because of the relatively high water pressure to which the bottom is subjected and thus the relatively wide bottom and top. Because the materials are loose, the earth-rock dam can bear the dynamic shaking of the foundation, but water can slowly permeate into the dam to reduce the firmness of the dam, so engineers can add a layer of waterproof clay on the surface of the dam or design a plurality of channels to allow water to flow away.
The horizontal crack (‌ transverse crack) is vertical or oblique to the axis of the dam, often appears at the top of the dam and stretches into the dam to a certain depth, and can seriously develop into the dam slope, even penetrate up and down stream to cause concentrated leakage, and directly endanger the safety of the dam. The reason for the creation of the transverse split is mainly the large uneven subsidence ‌ of the adjacent embankment block foundation.
The vertical cracks (‌ longitudinal cracks) are parallel or nearly parallel to the dam axis, and are mostly formed at the top of the dam or at the upper part of the dam slope, and gradually extend vertically to the inside of the dam body. The longitudinal cracks are generally longer than the transverse cracks, and if the longitudinal cracks are not treated in time, the dam can be inclined to fall off after rainwater invasion. Reasons for creating longitudinal cracks include staged heightening, compaction quality, and filler material differentiation ‌, among other factors.
The tortoise cracks are also called as net cracks or chaps, the cracks are connected into tortoise shell-shaped irregular cracks, and the length of the short side is not more than 40 cm. Parallel dense cracks exist in the longitudinal direction of the pavement, and the cracks are formed when the distance between the cracks is not more than 30cm although the cracks are not formed.
As shown in fig. 1, a dam crack monitoring and early warning system includes:
The system comprises a data acquisition module, a data processing module, a crack analysis module, a crack quality judging module, an image acquisition processing module and an early warning module;
the data acquisition module is used for acquiring dam related data and transmitting the acquired dam related data to the data processing module for processing, wherein the dam related data comprises a plurality of dam horizontal direction coordinates and a plurality of dam vertical direction coordinates;
The data acquisition module acquires dam related data by establishing a plane rectangular coordinate system, wherein the horizontal direction is an X axis, the vertical direction is a Y axis, matching the plane rectangular coordinate system with a dam, determining coordinates of points on a plurality of dams on the X axis as horizontal direction coordinates of the plurality of dams, and determining coordinates of points on the plurality of dams on the Y axis as vertical direction coordinates of the plurality of dams;
The data processing module processes data after receiving the dam related data sent by the data acquisition module, and specifically, the processing process of the data processing module comprises the following steps:
determining coordinates of points with the largest distance between two horizontal coordinates according to a plurality of dykes and dams horizontal direction coordinates in dykes and dams related data, marking the coordinates as two horizontal extreme points, and determining dykes and dams horizontal distance according to the coordinates of the two horizontal extreme points;
determining coordinates of points with the largest distance between two vertical coordinates according to a plurality of dykes and dams vertical direction coordinates in dykes and dams related data, marking the coordinates as two vertical extreme points, and determining dykes and dams vertical distance according to the two vertical extreme point coordinates;
wherein i is a dam related data collection number index, and i=1, 2,3, and n, n is a total number of dam related data collection numbers.
And calculating the dam comprehensive distance by using the obtained dam horizontal distance Si and the dam vertical distance Ci to obtain a dam comprehensive distance coefficient, wherein the dam comprehensive distance calculation process of the data processing module is as follows:
Wherein Zi is a dyke comprehensive distance coefficient, S0 is a preset standard horizontal coefficient, C0 is a preset standard vertical coefficient, k 1 is a horizontal distance influence coefficient, k 2 is a vertical distance influence coefficient, ln () is a logarithmic function based on a natural constant e, and t is a preset proportional coefficient;
Further, in the specific implementation process, the preset standard horizontal coefficient and the preset standard vertical coefficient are obtained through multiple simulation calculation and data average value acquisition after the horizontal distance and the vertical distance are obtained through daily acquisition;
In this embodiment, the horizontal distance influence coefficient and the vertical distance influence coefficient are obtained by performing comprehensive evaluation calculation according to the influence of external factors when the horizontal distance and the vertical distance are obtained daily, and the comprehensive evaluation calculation comprises artificial factors, machine detection, environmental factors and the like, wherein the artificial factors are caused by artificial operation or improper scanning;
The data processing module utilizes the calculated comprehensive distance coefficient Zi of the dykes to send the comprehensive distance coefficient Zi into the crack analysis module;
after receiving the dam comprehensive distance coefficient Zi sent by the data processing module, the crack analysis module performs crack analysis, and specifically, the analysis process of the crack analysis module comprises the following steps:
setting a dam comprehensive distance threshold Z0;
Comparing the dam comprehensive distance coefficient Zi with a dam comprehensive distance threshold Z0, determining whether a crack exists in the dam according to a comparison result, determining the type of the crack, and sending crack signals of different types to an image acquisition and processing module, wherein the process is as follows:
if Zi is less than or equal to Z0, judging that the dam does not have cracks, and sending a crack-free signal to an early warning module by a crack analysis module;
If Zi is greater than Z0, judging that the dam has cracks, and further determining the type of the cracks;
Setting a horizontal proportionality coefficient P1 and a vertical proportionality coefficient P2, wherein P2> P1>1;
In this embodiment, the horizontal scaling factor and the vertical scaling factor are obtained by performing multiple simulation calculation on the related data of the horizontal crack and the vertical crack according to the ratio of the related data to the set dam comprehensive distance threshold value when the related data of the horizontal crack and the vertical crack are obtained in daily life;
If it is The method includes the steps that at the moment, a crack only exists in the horizontal direction, the dam crack at the moment is judged to be the horizontal crack, a crack analysis module sends a horizontal crack signal to an image acquisition processing module, and a crack quality judgment signal is sent to a crack quality judgment module;
If it is If the dam crack is judged to be the vertical crack, the crack analysis module sends a vertical crack signal to the image acquisition processing module and sends a crack quality judgment signal to the crack quality judgment module;
If it is If the dam cracks are determined to be cracking cracks, the crack analysis module sends a tortoise crack signal to the image acquisition processing module and sends a crack quality determination signal to the crack quality determination module;
After receiving the crack quality judging signal, the crack quality judging module judges the quality of the dam crack, and specifically, the process of judging the quality of the crack quality judging module comprises the following steps:
The method comprises the steps of collecting crack related index data by a data collection center, wherein the crack related index data comprise crack size data and crack inclination angles, and the crack size data comprise crack length data, crack width data and crack depth data;
In this embodiment, the length of the crack is generally measured by a precision steel rule. The width of the crack is generally detected by a reading magnifying glass or an electronic width gauge. The depth of the crack has larger width and lower precision requirement, and can be estimated by inserting a feeler gauge into the crack, wherein for the crack with higher precision requirement, a special instrument is adopted for detection, and in order to improve the detection accuracy, a ‌ ultrasonic method ‌ is adopted for measuring the depth of the crack by utilizing the propagation speed and attenuation characteristic of ultrasonic waves in concrete;
Preprocessing the crack related index data to obtain processed crack related index data;
wherein, the pretreatment mainly comprises:
Data cleaning, data integration, data transformation, data reduction and the like. The data processing technology is used before data mining, so that the quality of a data mining mode is greatly improved, and the time required by actual mining is reduced.
In this embodiment, the data cleaning routine "cleans" the data by filling in missing values, smoothing noise data, identifying or deleting outliers and solving inconsistencies, mainly by data cleaning the crack-related index data. The method mainly achieves the following aims of format standardization, abnormal data clearing, error correction and repeated data clearing.
Marking the processed crack size data, wherein the crack length data is marked as Lj, the crack width data is marked as Dj, and the crack depth data is marked as Sj, wherein j is the collection number label of the crack size data, and j=1, 2,3, & gt, m and m are the collection number total number of the crack size data;
And calculating a crack comprehensive index by using the identified crack size data to obtain a crack comprehensive index size coefficient, wherein the crack comprehensive index calculation process of the crack quality judging module is as follows:
Wherein Fj is a crack comprehensive index size coefficient, R1 is a crack length influence coefficient, R2 is a crack width influence coefficient, R3 is a crack depth influence coefficient, and alpha is a preset correlation coefficient;
In this embodiment, the fracture length influence coefficient, the fracture width influence coefficient and the fracture depth influence coefficient are obtained by performing comprehensive evaluation calculation according to the influence of external factors when the fracture length data, the fracture width data and the fracture depth data are obtained daily, and the comprehensive evaluation calculation comprises artificial factors, machine detection, environmental factors and the like, wherein the artificial factors are caused by incorrect manual operation or scanning;
inputting the calculated size coefficient of the comprehensive crack index and the crack inclination angle into a pre-established crack standard judging model, and outputting to obtain a crack standard judging result;
The crack standard judging result comprises cracks of different grades, including a first-stage crack, a second-stage crack and a third-stage crack;
sending the crack standard discrimination result to an image acquisition processing module;
In this embodiment, the crack standard discrimination model is trained based on an artificial intelligence model;
training a crack standard discrimination model based on the artificial intelligence model:
Acquiring preset standard crack related indexes through a server, wherein the crack related indexes comprise preset standard crack sizes and preset standard crack dip angles;
Training an artificial intelligent model through preset standard fracture correlation indexes to obtain and store a fracture standard judgment model, wherein the artificial intelligent model comprises a deep convolutional neural network model and an RBF neural network model.
The image acquisition processing module acquires images corresponding to different types of crack signals as a first image and images corresponding to the crack standard discrimination results as a second image after receiving the different types of crack signals sent by the crack analysis module and the crack standard discrimination results sent by the crack quality discrimination module, and matches the first image with the second image to obtain a final crack image, wherein the types of the final crack image comprise a first-stage horizontal crack, a second-stage horizontal crack, a third-stage horizontal crack, a first-stage vertical crack, a second-stage vertical crack, a third-stage vertical crack, a first-stage tortoise crack, a second-stage tortoise crack and a third-stage tortoise crack;
and after receiving the final crack image sent by the image acquisition processing module, the early warning module carries out voice alarm prompt according to the grade type of the final crack image.
Specifically, the following examples are provided to further illustrate the present invention:
The detection equipment can flexibly move on the surface of the dam and go deep into difficult-to-reach areas to detect by designing a moving platform with multiple degrees of freedom (such as horizontal movement, vertical lifting, rotation and the like).
The data acquisition module and the crack data judging module comprise a plurality of sensor modules, and each sensor module is provided with an independent data processing unit and can analyze, acquire and preliminarily judge crack information in real time.
By designing the telescopic detection arm (how the detection arm can detect the depth and the width is needed to be considered), the length and the angle can be automatically adjusted according to the depth and the position of the crack, and the sensor can be ensured to accurately contact and detect the depth and the width of the crack. The probe arm may incorporate optical fibers or wires inside for transmitting data and power. Thereby realizing the acquisition and detection of the depth of the crack and the width of the crack;
and in particular, an automatic warning and marking device can be designed, and when the crack is detected, an acoustic alarm can be automatically triggered. Meanwhile, a mark spraying mechanism is arranged, and marking liquids with different colors are automatically sprayed according to the size of the crack, so that the subsequent treatment is facilitated.
An embodiment II is a dam crack monitoring and early warning method, which comprises the following steps:
S101, acquiring dyke related data, wherein the dyke related data comprises a plurality of dyke horizontal direction coordinates and a plurality of dyke vertical direction coordinates, carrying out coordinate distance processing on the dyke related data, and then carrying out dyke comprehensive distance calculation to obtain dyke comprehensive distance coefficients;
S102, setting a dyke comprehensive distance threshold, judging that no crack exists if the dyke comprehensive distance coefficient is smaller than or equal to the dyke comprehensive distance threshold, and judging that the crack exists if the dyke comprehensive distance coefficient is larger than the dyke comprehensive distance threshold;
s103, setting a horizontal proportion coefficient and a vertical proportion coefficient, comparing the set coefficient with the dam comprehensive distance coefficient according to the product of the set coefficient and the dam comprehensive distance threshold value, and determining the crack type according to the comparison result, wherein the vertical proportion coefficient is larger than the horizontal proportion coefficient and larger than one;
s104, acquiring and preprocessing crack related index data, and obtaining the processed crack related index data, wherein the crack related index data comprises crack size data and a crack inclination angle, and then carrying out crack comprehensive index calculation by using the crack size data to obtain a crack comprehensive index size coefficient;
S105, inputting the size coefficient of the comprehensive crack index and the inclination angle of the crack into a pre-established crack standard judging model, outputting to obtain a crack standard judging result, matching the image of the crack standard judging result with images of different crack types to obtain a final crack image, and carrying out voice alarm prompt according to the grade type of the final crack image.
The invention also provides a computer device comprising one or more processors and a memory for storing one or more computer programs, the programs comprising program instructions, the processors for executing the program instructions stored by the memory, based on the same inventive concept. The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processor, digital signal processor (DIGITAL SIGNAL Processor, DSP), application specific integrated circuit (Application SpecificIntegrated Circuit, ASIC), field-Programmable gate array (Field-Programmable GATEARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc., that are the computational core and control core of the terminal for implementing one or more instructions, particularly for loading and executing one or more instructions within a computer storage medium to implement the methods described above.
It should be further noted that, based on the same inventive concept, the present invention also provides a computer storage medium having a computer program stored thereon, which when executed by a processor performs the above method. The storage media may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas which are obtained by acquiring a large amount of data and performing software simulation to obtain the closest actual situation, and preset parameters and preset thresholds in the formulas are set by a person skilled in the art according to the actual situation or are obtained by simulating a large amount of data.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing has shown and described the basic principles, principal features, and advantages of the present disclosure. It will be understood by those skilled in the art that the present disclosure is not limited to the embodiments described above, which have been described in the foregoing and description merely illustrates the principles of the disclosure, and that various changes and modifications may be made therein without departing from the spirit and scope of the disclosure, which is defined in the appended claims.

Claims (9)

1.一种堤坝裂缝监测预警系统，其特征在于，包括：1. A dam crack monitoring and early warning system, comprising: 裂缝分析模块：用于设定堤坝综合距离阈值，将堤坝综合距离系数与堤坝综合距离阈值进行比较，根据比较结果确定堤坝是否存在裂缝，如存在裂缝，则通过设定水平比例系数和垂直比例系数后再次比较，根据再次比较结果确定裂缝类型，并将不同类型的裂缝信号发送至图像采集处理模块，并发送裂缝质量判定信号至裂缝质量判定模块；Crack analysis module: used to set the comprehensive distance threshold of the dam, compare the comprehensive distance coefficient of the dam with the comprehensive distance threshold of the dam, and determine whether there are cracks in the dam according to the comparison result. If there are cracks, the horizontal scale coefficient and the vertical scale coefficient are set and then compared again. The crack type is determined according to the comparison result, and different types of crack signals are sent to the image acquisition and processing module, and the crack quality determination signal is sent to the crack quality determination module; 其中，所述堤坝综合距离系数的计算过程如下：The calculation process of the comprehensive distance coefficient of the dam is as follows: 式中，Si为堤坝水平距离，Ci堤坝垂直距离，为Zi为堤坝综合距离系数，S0为预设标准水平系数，C0为预设标准垂直系数，k1为水平距离影响系数，k2为垂直距离影响系数，ln（）为以自然常数e为底数的对数函数，t为预设比例系数；其中，i为堤坝相关数据采集次数标号，且i=1、2、3、...、n，n为堤坝相关数据采集次数总数；In the formula, Si is the horizontal distance of the dam, Ci is the vertical distance of the dam, Zi is the comprehensive distance coefficient of the dam, S0 is the preset standard horizontal coefficient, C0 is the preset standard vertical coefficient, k1 is the horizontal distance influence coefficient, k2 is the vertical distance influence coefficient, ln() is the logarithmic function with the natural constant e as the base, and t is the preset proportional coefficient; where i is the number of times the dam-related data is collected, and i=1, 2, 3,..., n, and n is the total number of times the dam-related data is collected; 裂缝质量判定模块：用于采集裂缝相关指标数据，包括裂缝尺寸数据以及裂缝倾角，对裂缝相关指标数据进行预处理，得到处理后的裂缝相关指标数据，对处理后的裂缝尺寸数据进行标识处理，利用标识后的裂缝尺寸数据进行裂缝综合指标计算，得出裂缝综合指标尺寸系数，将裂缝综合指标尺寸系数以及裂缝倾角输入至预先建立的裂缝标准判别模型内，输出得到裂缝标准判别结果，将裂缝标准判别结果发送至图像采集处理模块；Crack quality determination module: used to collect crack-related index data, including crack size data and crack inclination, pre-process the crack-related index data to obtain processed crack-related index data, mark the processed crack size data, calculate the crack comprehensive index using the marked crack size data, obtain the crack comprehensive index size coefficient, input the crack comprehensive index size coefficient and the crack inclination into a pre-established crack standard discrimination model, output the crack standard discrimination result, and send the crack standard discrimination result to the image acquisition and processing module; 图像采集处理模块：用于采集不同类型的裂缝信号所对应的图像作为第一图像，以及裂缝标准判别结果所对应的图像作为第二图像，将第一图像与第二图像进行匹配，得出最终裂缝图像，将最终裂缝图像发送至预警模块；Image acquisition and processing module: used to acquire images corresponding to different types of crack signals as the first image, and images corresponding to the crack standard discrimination results as the second image, match the first image with the second image, obtain the final crack image, and send the final crack image to the early warning module; 预警模块：用于根据最终裂缝图像的等级类型，进行语音报警提示。Early warning module: used to issue voice alarm prompts according to the grade type of the final crack image. 2.根据权利要求1所述的一种堤坝裂缝监测预警系统，其特征在于，还包括：2. A dam crack monitoring and early warning system according to claim 1, characterized in that it also includes: 数据采集模块：用于采集堤坝相关数据，将堤坝相关数据发送至数据处理模块，其中，所述堤坝相关数据包括：多个堤坝水平方向坐标以及多个堤坝垂直方向坐标；Data acquisition module: used to collect dam-related data and send the dam-related data to the data processing module, wherein the dam-related data includes: a plurality of dam horizontal coordinates and a plurality of dam vertical coordinates; 数据处理模块：用于根据堤坝相关数据进行坐标距离处理，得到堤坝水平距离和堤坝垂直距离，将堤坝水平距离和堤坝垂直距离进行堤坝综合距离计算，得出堤坝综合距离系数，将堤坝综合距离系数发送至裂缝分析模块。Data processing module: used to process coordinate distance according to dam-related data, obtain horizontal distance and vertical distance of dam, calculate comprehensive distance of dam based on horizontal distance and vertical distance of dam, obtain comprehensive distance coefficient of dam, and send comprehensive distance coefficient of dam to crack analysis module. 3.根据权利要求2所述的一种堤坝裂缝监测预警系统，其特征在于，所述数据处理模块的数据处理模块根据堤坝相关数据进行坐标距离处理的过程：3. A dam crack monitoring and early warning system according to claim 2, characterized in that the data processing module of the data processing module performs coordinate distance processing according to dam related data: 根据堤坝相关数据内的多个堤坝水平方向坐标，确定两个水平坐标相距最大的点的坐标，标记为两个水平极值点，根据两个水平极值点坐标确定堤坝水平距离；According to the horizontal coordinates of multiple dams in the dam-related data, the coordinates of the points with the largest distance between the two horizontal coordinates are determined, marked as two horizontal extreme value points, and the horizontal distance of the dam is determined according to the coordinates of the two horizontal extreme value points; 根据堤坝相关数据内的多个堤坝垂直方向坐标，确定两个垂直坐标相距最大的点的坐标，标记为两个垂直极值点，根据两个垂直极值点坐标确定堤坝垂直距离。According to the vertical coordinates of multiple dams in the dam-related data, the coordinates of the points with the largest vertical coordinate distance are determined, marked as two vertical extreme points, and the vertical distance of the dam is determined according to the coordinates of the two vertical extreme points. 4.根据权利要求1所述的一种堤坝裂缝监测预警系统，其特征在于，所述裂缝分析模块的分析过程包括：4. A dam crack monitoring and early warning system according to claim 1, characterized in that the analysis process of the crack analysis module includes: 设定堤坝综合距离阈值Z0；Set the comprehensive distance threshold of the embankment Z0; 若Zi≤Z0，则判定堤坝不存在裂缝，裂缝分析模块发送无裂缝信号至预警模块；If Zi≤Z0, it is determined that there are no cracks in the dam, and the crack analysis module sends a no-crack signal to the early warning module; 若Zi＞Z0，则判定堤坝存在裂缝，进一步确定裂缝类型；If Zi＞Z0, it is determined that there are cracks in the dam, and the type of cracks is further determined; 设定水平比例系数P1和垂直比例系数P2；其中，P2＞P1＞1；Set the horizontal scale factor P1 and the vertical scale factor P2; wherein P2＞P1＞1; 若，则判定此时的堤坝裂缝为水平裂缝，发送水平裂缝信号至图像采集处理模块，like , then the dam crack is determined to be a horizontal crack, and a horizontal crack signal is sent to the image acquisition and processing module. 若，则判定此时的堤坝裂缝为垂直裂缝，发送垂直裂缝信号至图像采集处理模块，like , then the dam crack is determined to be a vertical crack, and a vertical crack signal is sent to the image acquisition and processing module. 若，则判定此时的堤坝裂缝为龟裂缝，发送龟裂缝信号至图像采集处理模块；like , then the dam crack is determined to be a tortoise crack, and a tortoise crack signal is sent to the image acquisition and processing module; 并且，发送裂缝质量判定信号至裂缝质量判定模块。Furthermore, a crack quality determination signal is sent to the crack quality determination module. 5.根据权利要求1所述的一种堤坝裂缝监测预警系统，其特征在于，所述裂缝质量判定模块对处理后的裂缝尺寸数据进行标识处理的过程：5. A dam crack monitoring and early warning system according to claim 1, characterized in that the crack quality determination module performs a marking process on the processed crack size data: 将裂缝长度数据标记为Lj，将裂缝宽度数据标记为Dj，将裂缝深度数据标记为Sj，其中，j为裂缝尺寸数据的采集数量标号，且j=1、2、3、...、m，m为裂缝尺寸数据的采集数量总数。The crack length data is marked as Lj, the crack width data is marked as Dj, and the crack depth data is marked as Sj, where j is the number of crack size data collected, and j=1, 2, 3, ..., m, and m is the total number of crack size data collected. 6.根据权利要求5所述的一种堤坝裂缝监测预警系统，其特征在于，所述裂缝质量判定模块的裂缝综合指标计算过程如下：6. A dam crack monitoring and early warning system according to claim 5, characterized in that the crack comprehensive index calculation process of the crack quality determination module is as follows: 式中，Fj为裂缝综合指标尺寸系数，R1为裂缝长度影响系数，R2为裂缝宽度影响系数，R3为裂缝深度影响系数，α为预设相关系数。Where Fj is the comprehensive index size coefficient of cracks, R1 is the influence coefficient of crack length, R2 is the influence coefficient of crack width, R3 is the influence coefficient of crack depth, and α is the preset correlation coefficient. 7.根据权利要求6所述的一种堤坝裂缝监测预警系统，其特征在于，所述裂缝质量判定模块的裂缝标准判别模型基于人工智能模型训练的过程：7. A dam crack monitoring and early warning system according to claim 6, characterized in that the crack standard discrimination model of the crack quality judgment module is based on the process of artificial intelligence model training: 通过服务器获取预设的标准裂缝相关指标，其中，所述裂缝相关指标包括预设标准裂缝尺寸以及预设标准裂缝倾角；Obtaining preset standard crack related indicators through a server, wherein the crack related indicators include a preset standard crack size and a preset standard crack inclination; 通过预设的标准裂缝相关指标对人工智能模型进行训练，获取并存储得到裂缝标准判别模型。The artificial intelligence model is trained through preset standard crack-related indicators to obtain and store a crack standard discrimination model. 8.根据权利要求1所述的一种堤坝裂缝监测预警系统，其特征在于，所述裂缝标准判别结果包括：第一级裂缝、第二级裂缝以及第三级裂缝；8. A dam crack monitoring and early warning system according to claim 1, characterized in that the crack standard identification results include: first-level cracks, second-level cracks and third-level cracks; 最终裂缝图像的类型包括：第一级水平裂缝、第二级水平裂缝、第三级水平裂缝、第一级垂直裂缝、第二级垂直裂缝、第三级垂直裂缝、第一级龟裂缝、第二级龟裂缝、第三级龟裂缝。The types of the final crack images include: first-level horizontal cracks, second-level horizontal cracks, third-level horizontal cracks, first-level vertical cracks, second-level vertical cracks, third-level vertical cracks, first-level tortoise cracks, second-level tortoise cracks, and third-level tortoise cracks. 9.一种堤坝裂缝监测预警方法，其特征在于，方法包括以下步骤：9. A dam crack monitoring and early warning method, characterized in that the method comprises the following steps: 获取堤坝相关数据，包括多个堤坝水平方向坐标以及多个堤坝垂直方向坐标，对堤坝相关数据进行坐标距离处理，然后进行堤坝综合距离计算，得出堤坝综合距离系数；Obtain dam-related data, including multiple horizontal dam coordinates and multiple vertical dam coordinates, perform coordinate distance processing on the dam-related data, and then perform dam comprehensive distance calculation to obtain the dam comprehensive distance coefficient; 其中，所述堤坝综合距离系数的计算过程如下：The calculation process of the comprehensive distance coefficient of the dam is as follows: 式中，Si为堤坝水平距离，Ci堤坝垂直距离，为Zi为堤坝综合距离系数，S0为预设标准水平系数，C0为预设标准垂直系数，k1为水平距离影响系数，k2为垂直距离影响系数，ln（）为以自然常数e为底数的对数函数，t为预设比例系数；其中，i为堤坝相关数据采集次数标号，且i=1、2、3、...、n，n为堤坝相关数据采集次数总数；In the formula, Si is the horizontal distance of the dam, Ci is the vertical distance of the dam, Zi is the comprehensive distance coefficient of the dam, S0 is the preset standard horizontal coefficient, C0 is the preset standard vertical coefficient, k1 is the horizontal distance influence coefficient, k2 is the vertical distance influence coefficient, ln() is the logarithmic function with the natural constant e as the base, and t is the preset proportional coefficient; where i is the number of times the dam-related data is collected, and i=1, 2, 3,..., n, and n is the total number of times the dam-related data is collected; 设定堤坝综合距离阈值，若堤坝综合距离系数小于等于堤坝综合距离阈值，则判定不存在裂缝，若堤坝综合距离系数大于堤坝综合距离阈值，则判定存在裂缝；A dam comprehensive distance threshold is set. If the dam comprehensive distance coefficient is less than or equal to the dam comprehensive distance threshold, it is determined that there is no crack. If the dam comprehensive distance coefficient is greater than the dam comprehensive distance threshold, it is determined that there is a crack. 设定水平比例系数和垂直比例系数，根据设定系数与堤坝综合距离阈值的乘积与堤坝综合距离系数进行比较，根据比较结果确定裂缝类型，其中，垂直比例系数大于水平比例系数大于一；A horizontal scale factor and a vertical scale factor are set, and the product of the set coefficient and the embankment comprehensive distance threshold is compared with the embankment comprehensive distance coefficient, and the crack type is determined according to the comparison result, wherein the vertical scale factor is greater than the horizontal scale factor and is greater than one; 获取裂缝相关指标数据并进行预处理，得到处理后的裂缝相关指标数据，其中，裂缝相关指标数据包括裂缝尺寸数据以及裂缝倾角，再利用裂缝尺寸数据进行裂缝综合指标计算，得出裂缝综合指标尺寸系数；Acquire crack-related index data and perform preprocessing to obtain processed crack-related index data, wherein the crack-related index data includes crack size data and crack inclination, and then use the crack size data to calculate the crack comprehensive index to obtain the crack comprehensive index size coefficient; 将裂缝综合指标尺寸系数与裂缝倾角输入至预先建立的裂缝标准判别模型内，输出得到裂缝标准判别结果，根据裂缝标准判别结果的图像与不同裂缝类型的图像进行匹配，得出最终裂缝图像，根据最终裂缝图像的等级类型，进行语音报警提示。The comprehensive crack index size coefficient and crack inclination are input into the pre-established crack standard discrimination model, and the crack standard discrimination result is output. The image of the crack standard discrimination result is matched with the images of different crack types to obtain the final crack image. According to the grade type of the final crack image, a voice alarm prompt is given.
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